Observability and Monitoring in AIOps

To prevent outages and maintain the uptime of business-critical apps, DevOps teams must automate monitoring of observability data from their software stack.

September 22, 2020 4 minute read

What is observability? 

Observability is the ability to infer internal states of a system based on the system’s external outputs. System observability is the method for evaluating outputs to reach meaningful conclusions about internal states of the system.

What is monitoring?

Observability is the ability to infer a system’s internal states. Monitoring is defined as the actions involved in observability: observing the quality of system performance over a time duration. The monitoring action, which tools and processes support, can describe the performance, health and relevant characteristics of a system’s internal states.

Observability and monitoring are complementary

Monitoring and observability are two distinct practices. Observability isn’t a substitute for monitoring. They are entirely complementary; you can’t have one without the other.

In order to prevent outages and maintain the uptime of business-critical apps, it’s key for DevOps teams to monitor the observability data from their software development and deployment toolset.

Collecting and analyzing observability data — metrics, logs and traces — in real-time provides the cues, signals and insights DevOps teams need to build their service assurance strategy. 

Google’s SRE Book states that monitoring systems should address two questions: What’s broken? Why is it broken? “‘What’ versus ‘why’ is one of the most important distinctions in writing good monitoring with maximum signal and minimum noise,” reads the book.

Think monitoring is passe? Think again.

Simply put, observability is achieved when data is made available from within the system that you wish to monitor. Monitoring is the actual task of collecting and displaying this data.

Monitoring was traditionally the way of life for operations engineers, who had to deal with floods of ‘up/down’ alerts indicating system availability. A decade ago, monitoring tools did little more than generating ‘up/down’ checks. Since the birth of cloud computing and observability, this is no longer the case. But, it has also paved the way for an AI-led operations evolution, known as artificial intelligence for IT operations (AIOps).

The ability to disseminate and observe what’s going on within your applications and services is often met with a steady flow of metrics, logs and traces. But the data alone contain little information because they lack context. For example, knowing that the CPU usage on a server is at 84 percent means nothing if you don’t know whether this level indicates normal operating behavior or a potential problem. You must understand the context and much more.

  • What was it like yesterday? Understanding performance over time provides a more comprehensive picture.
  • What was it while doing something different? Understanding server loads by task helps weight performance levels — and whether they are an issue.
  • Is this unique? Is it a standalone element or part of ephemeral or autoscaling logic?

Producing metrics, logs and traces is clearly just one part of the equation. Monitoring this data is the next key part of the equation to fill in the context. The use of AIOps helps automate your monitoring and discover unknown-unknowns. Only by combining observability with AIOps will you achieve true operational scale and automation.

Observability with AIOps automates monitoring

By applying AIOps to all of your metrics, logs and traces, you can achieve more effective operations management by getting the complete picture for service assurance automatically.

This includes:

  • Monitoring and applying AI and machine learning algorithms to all the data.
  • Detecting anomalies.
  • Surfacing significant and important events.
  • Correlating alerts.
  • Providing incidents with context so you can collaborate.
  • Identifying the probable root cause for automated remediation.

Your observations can lead you to the answers. The process of examining evidence to be able to find the cues and signals requires a good understanding of your applications, services and domain, as well as a good sense of intuition.

Embrace AIOps to examine the evidence to surface the cues and signals for you. Only AIOps will provide the context and awareness required for automated remediation and outage avoidance. Let AI operate so you can develop more and focus on the customer experience.

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